Background: The size of genomics data has been growing rapidly over the last decade. However, the conventional data analysis techniques are incapable of processing this huge amount of data. For the efficient processing of high dimensional datasets, it is essential to develop some new parallel methods.
Methods: In this work, a novel distributed method is presented using Map-Reduce (MR)-based approach. The proposed algorithm consists of MR-based Fisher score (mrFScore), MR-based ReliefF (mrRefiefF), and MR-based probabilistic neural network (mrPNN) using a weighted chaotic grey wolf optimization technique (WCGWO). Here, mrFScore, and mrRefiefF methods are introduced for feature selection (FS), and mrPNN is implemented as an effective method for microarray classification. The proper choice of smoothing parameter (σ) plays a major role in the prediction ability of the PNN which is addressed using a novel technique namely, WCGWO. The WCGWO algorithm is used to select the optimal value of σ in PNN.
Results: These algorithms have been successfully implemented using the Hadoop framework. The proposed model is tested by using three large and one small microarray datasets, and a comparative analysis is carried out with the existing FS and classification techniques. The results suggest that WCGWO-mrPNN can outperform other methods for high dimensional microarray classification.
Conclusion: The effectiveness of the proposed methods are compared with other existing schemes. Experimental results reveal that the proposed scheme is accurate and robust. Hence, the suggested scheme is considered to be a reliable framework for microarray data analysis.
Significance: Such a method promotes the application of parallel programming using Hadoop cluster for the analysis of large-scale genomics data, particularly when the dataset is of high dimension.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.cmpb.2020.105625 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Development of a Genetic Test Indicating Increased AVP/V1b Signalling in Patients with Acute Depression.
Pharmacopsychiatry
January 2025
Max Planck Institute of Psychiatry, Munich, Germany.
A subgroup of patients with acute depression show an impaired regulation of the hypothalamic-pituitary-adrenocortical axis, which can be sensitively diagnosed with the combined dexamethasone (dex)/corticotropin releasing hormone (CRH)-test. This neuropathological alteration is assumed to be a result of hyperactive AVP/V1b signalling. Given the complicated procedure of the dex/CRH-test, this study aimed to develop a genetic variants-based alternative approach to predict the outcome of the dex/CRH-test in acute depression.
View Article and Find Full Text PDFMachine-Learning Predictions of Cochlear Implant Functional Outcomes: A Systematic Review.
Ear Hear
January 2025
San Francisco Department of Otolaryngology - Head and Neck Surgery, University of California, San Francisco, California, USA.
Objectives: Cochlear implant (CI) user functional outcomes are challenging to predict because of the variability in individual anatomy, neural health, CI device characteristics, and linguistic and listening experience. Machine learning (ML) techniques are uniquely poised for this predictive challenge because they can analyze nonlinear interactions using large amounts of multidimensional data. The objective of this article is to systematically review the literature regarding ML models that predict functional CI outcomes, defined as sound perception and production.
View Article and Find Full Text PDFBeta oscillations predict the envelope sharpness in a rhythmic beat sequence.
Sci Rep
January 2025
RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Forskningsveien 3A, Oslo, 0373, Norway.
Periodic sensory inputs entrain oscillatory brain activity, reflecting a neural mechanism that might be fundamental to temporal prediction and perception. Most environmental rhythms and patterns in human behavior, such as walking, dancing, and speech do not, however, display strict isochrony but are instead quasi-periodic. Research has shown that neural tracking of speech is driven by modulations of the amplitude envelope, especially via sharp acoustic edges, which serve as prominent temporal landmarks.
View Article and Find Full Text PDFMultiscale Analysis of Alzheimer's Disease Using Feature Fusion in Cognitive and Sensory Brain Regions.
Digit Biomark
December 2024
Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI, USA.
Introduction: This research is focused on early detection of Alzheimer's disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from magnetic resonance imaging and positron emission tomography images.
Methods: Using 2D gray level co-occurrence matrix (2D-GLCM) texture features, volume, standardized uptake value ratios (SUVR), and obesity from different neuroimaging modalities, the study applies various classifiers, demonstrating a feature importance analysis in each region of interest. The research employs four classifiers, namely linear support vector machine, linear discriminant analysis, logistic regression (LR), and logistic regression with stochastic gradient descent (LRSGD) classifiers, to determine feature importance, leading to subsequent validation using a probabilistic neural network classifier.
TCN-Transformer Deep Network with Random Forest for Prediction of the Chemical Synthetic Ammonia Process.
ACS Omega
January 2025
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
It is of great significance to realize the accurate prediction of the key output response of the chemical synthetic ammonia process for optimizing system performance and operation monitoring. Because many key intermediate variables of complex systems are difficult to measure comprehensively, there are great difficulties and errors in mechanism analysis and identification modeling techniques. Based on random forest (RF) variable selection, a deep neural network combining temporal convolutional network (TCN) and transformer is proposed to predict the output variables of the synthetic ammonia process.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!